isaUnderlying

Determine if tall array data is of specified class

Syntax

tf = isaUnderlying(X,cl)

Description

tf = isaUnderlying(X,cl) returns a tall array containing logical 1 (true) if the underlying data in tall array X is of class cl. Otherwise, the tall array contains logical 0 (false).

Examples

Test Underlying Class of Tall Arrays

All tall tables and arrays belong to the tall class. However, the underlying data type of a tall array can vary.

Create a datastore for the airlinesmall.csv data set. Select a subset of the variables to work with, and treat 'NA' values as missing data so that datastore replaces them with NaN values. Convert the datastore into a tall table.

varnames = {'Year', 'UniqueCarrier'};
ds = datastore('airlinesmall.csv','TreatAsMissing','NA',...
    'SelectedVariableNames',varnames);
tt = tall(ds)

tt =

  Mx2 tall table

    Year    UniqueCarrier
    ____    _____________

    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
    1987        'PS'     
     :            :
     :            :

Test whether the underlying data type of the first table variable Year is single.

tf = isaUnderlying(tt.Year,'single')

tf =

  tall logical

   0

Determine the actual underlying data type of Year.

udt = classUnderlying(tt.Year)

udt =

  1x6 tall char array

    'double'

Input Arguments

X — Input array

tall array

Input array, specified as a tall array.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | table | cell | categorical | datetime | duration | calendarDuration

cl — Underlying class

character vector

Underlying class, specified as a character vector specifying any valid MATLAB^® class name.

Example: tf = isaUnderlying(X,'double')

Tips

• Use classUnderlying to determine the underlying data type of a tall array.

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays

Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).